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half way through crossterm objective

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This commit is contained in:
Max Zwiessele 2013-11-14 09:01:05 +00:00
parent e7dc17a5cd
commit d95137e049
3 changed files with 31 additions and 11 deletions
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17
GPy/kern/kern.py
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@ -462,10 +462,8 @@ class kern(Parameterized):
pass pass
# rbf X bias # rbf X bias
elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)): elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, (RBF, RBFInv)):
target += 2 * p1.variance * (p2._psi1[:, :, None] + p2._psi1[:, None, :]) target += p1.variance * (p2._psi1[:, :, None] + p2._psi1[:, None, :])
elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)): elif isinstance(p2, (Bias, Fixed)) and isinstance(p1, (RBF, RBFInv)):
tmp1 = p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
renorm = p1.variance*np.exp()
target += p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :]) target += p2.variance * (p1._psi1[:, :, None] + p1._psi1[:, None, :])
# linear X bias # linear X bias
elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear): elif isinstance(p1, (Bias, Fixed)) and isinstance(p2, Linear):
@ -478,12 +476,21 @@ class kern(Parameterized):
target += p2.variance * (tmp[:, :, None] + tmp[:, None, :]) target += p2.variance * (tmp[:, :, None] + tmp[:, None, :])
# rbf X any # rbf X any
elif isinstance(p1, (RBF, RBFInv)): elif isinstance(p1, (RBF, RBFInv)):
pass psi11 = np.zeros((mu.shape[0], Z.shape[0]))
psi12 = np.zeros((mu.shape[0], Z.shape[0]))
p1.psi1(Z, mu, S, psi11)
p2.psi1(Z, mu, S, psi12)
crossterms = psi11[:, :, None] + psi12[:, None, :]
crossterms += psi12[:, :, None] + psi11[:, None, :]
target += p1._crossterm_product_expectation(p2, Z, mu, S)
#import ipdb;ipdb.set_trace()
elif isinstance(p2, (RBF, RBFInv)): elif isinstance(p2, (RBF, RBFInv)):
raise NotImplementedError # TODO raise NotImplementedError # TODO
else: else:
raise NotImplementedError, "psi2 cannot be computed for this kernel" raise NotImplementedError, "psi2 cannot be computed for this kernel"
return target return target
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S): def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S):
target = np.zeros(self.num_params) target = np.zeros(self.num_params)

10
GPy/kern/parts/rbf.py
View file

@ -208,6 +208,16 @@ class RBF(Kernpart):
self._psi_computations(Z, mu, S) self._psi_computations(Z, mu, S)
target += self._psi2 target += self._psi2
def _crossterm_product_expectation(self, K, Z, mu, S):
# compute the crossterm expectation for K as the other kernel:
import ipdb;ipdb.set_trace()
Sigma = 1./self.lengthscale[None,:] + 1./S # is independent across M,
M = (Z[None,:,:]/self.lengthscale[None,None,:] + (mu/S)[:,None,:]) / Sigma[:,None,:]
psi1_other = K.psi1()
self.variance
# return is [N x M x M]
return
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target): def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
"""Shape N,num_inducing,num_inducing,Ntheta""" """Shape N,num_inducing,num_inducing,Ntheta"""
self._psi_computations(Z, mu, S) self._psi_computations(Z, mu, S)

15
GPy/testing/psi_stat_expectation_tests.py
View file

@ -27,7 +27,7 @@ def ard(p):
@testing.deepTest(__test__()) @testing.deepTest(__test__())
class Test(unittest.TestCase): class Test(unittest.TestCase):
input_dim = 9 input_dim = 9
num_inducing = 4 num_inducing = 13
N = 30 N = 30
Nsamples = 9e6 Nsamples = 9e6
@ -51,13 +51,16 @@ class Test(unittest.TestCase):
# GPy.kern.bias(self.input_dim) + # GPy.kern.bias(self.input_dim) +
# GPy.kern.white(self.input_dim)), # GPy.kern.white(self.input_dim)),
# (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) + # (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
# GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) + (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
# GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) + +GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True)
# GPy.kern.bias(self.input_dim) + # GPy.kern.bias(self.input_dim) +
# GPy.kern.white(self.input_dim)), # GPy.kern.white(self.input_dim)),
(GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) + ),
GPy.kern.bias(self.input_dim, np.random.rand()) + (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
GPy.kern.white(self.input_dim, np.random.rand())), +GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
#+GPy.kern.bias(self.input_dim, np.random.rand())
#+GPy.kern.white(self.input_dim, np.random.rand())),
),
(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) + (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
GPy.kern.bias(self.input_dim, np.random.rand()) + GPy.kern.bias(self.input_dim, np.random.rand()) +
GPy.kern.white(self.input_dim, np.random.rand())), GPy.kern.white(self.input_dim, np.random.rand())),